Ian En-Hsu Yen

50 papers A* 18A 5B 1Journal 17Unranked 8

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding. ICLR Ranajoy Sadhukhan, Jian Chen, Zhuoming Chen, Vashisth Tiwari, Ruihang Lai, Jinyuan Shi, Ian En-Hsu Yen, Avner May, Tianqi Chen, Beidi Chen
2025	J	`jnl`	SQ-format: A Unified Sparse-Quantized Hardware-friendly Data Format for LLMs. CoRR Ruixuan Huang, Hao Zeng, Hantao Huang, Jinyuan Shi, Minghui Yu, Ian En-Hsu Yen, Shuai Wang
2024	J	`jnl`	MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding. CoRR Jian Chen, Vashisth Tiwari, Ranajoy Sadhukhan, Zhuoming Chen, Jinyuan Shi, Ian En-Hsu Yen, Beidi Chen
2023	J	`jnl`	FP8-BERT: Post-Training Quantization for Transformer. CoRR Jianwei Li, Tianchi Zhang, Ian En-Hsu Yen, Dongkuan Xu
2022	J	`jnl`	S4: a High-sparsity, High-performance AI Accelerator. CoRR Ian En-Hsu Yen, Zhibin Xiao, Dongkuan Xu
2022	—	`conf`	Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm. ACL (1) Shaoyi Huang, Dongkuan Xu, Ian En-Hsu Yen, Yijue Wang, Sung-En Chang, Bingbing Li, Shiyang Chen, Mimi Xie, Sanguthevar Rajasekaran, Hang Liu, Caiwen Ding
2022	B	`conf`	Towards ℓ1 Regularization for Deep Neural Networks: Model Sparsity Versus Task Difficulty. DSAA Ta-Chun Shen, Chun-Pai Yang, Ian En-Hsu Yen, Shou-De Lin
2021	—	`conf`	Rethinking Network Pruning - under the Pre-train and Fine-tune Paradigm. NAACL-HLT Dongkuan Xu, Ian En-Hsu Yen, Jinxi Zhao, Zhibin Xiao
2021	J	`jnl`	Rethinking Network Pruning - under the Pre-train and Fine-tune Paradigm. CoRR Dongkuan Xu, Ian En-Hsu Yen, Jinxi Zhao, Zhibin Xiao
2021	J	`jnl`	Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm. CoRR Shaoyi Huang, Dongkuan Xu, Ian En-Hsu Yen, Sung-En Chang, Bingbing Li, Shiyang Chen, Mimi Xie, Hang Liu, Caiwen Ding
2020	A*	`conf`	Minimizing FLOPs to Learn Efficient Sparse Representations. ICLR Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos
2020	J	`jnl`	Minimizing FLOPs to Learn Efficient Sparse Representations. CoRR Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos
2019	A*	`conf`	Efficient Global String Kernel with Random Features: Beyond Counting Substructures. KDD Lingfei Wu, Ian En-Hsu Yen, Siyu Huo, Liang Zhao, Kun Xu, Liang Ma, Shouling Ji, Charu C. Aggarwal
2019	J	`jnl`	Efficient Global String Kernel with Random Features: Beyond Counting Substructures. CoRR Lingfei Wu, Ian En-Hsu Yen, Siyu Huo, Liang Zhao, Kun Xu, Liang Ma, Shouling Ji, Charu C. Aggarwal
2019	A*	`conf`	Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding. KDD Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang, Kun Xu, Liang Zhao, Xi Peng, Yinglong Xia, Charu C. Aggarwal
2019	J	`jnl`	Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding. CoRR Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang, Kun Xu, Liang Zhao, Xi Peng, Yinglong Xia, Charu C. Aggarwal
2019	J	`jnl`	Temporal popularity prediction of locations for geographical placement of retail stores. Knowl. Inf. Syst. Hsun-Ping Hsieh, Fandel Lin, Cheng-Te Li, Ian En-Hsu Yen, Hsin-Yu Chen
2018	J	`jnl`	D2KE: From Distance to Kernel and Embedding. CoRR Lingfei Wu, Ian En-Hsu Yen, Fangli Xu, Pradeep Ravikumar, Michael Witbrock
2018	J	`jnl`	Learning Tensor Latent Features. CoRR Sung-En Chang, Xun Zheng, Ian En-Hsu Yen, Pradeep Ravikumar, Rose Yu
2018	A*	`conf`	Loss Decomposition for Fast Learning in Large Output Spaces. ICML Ian En-Hsu Yen, Satyen Kale, Felix X. Yu, Daniel Niels Holtmann-Rice, Sanjiv Kumar, Pradeep Ravikumar
2018	A*	`conf`	MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization. NeurIPS Ian En-Hsu Yen, Wei-Cheng Lee, Kai Zhong, Sung-En Chang, Pradeep Ravikumar, Shou-De Lin
2018	A	`conf`	Random Warping Series: A Random Features Method for Time-Series Embedding. AISTATS Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi, Fangli Xu, Qi Lei, Michael Witbrock
2018	J	`jnl`	Random Warping Series: A Random Features Method for Time-Series Embedding. CoRR Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi, Fangli Xu, Qi Lei, Michael Witbrock
2018	A*	`conf`	Representer Point Selection for Explaining Deep Neural Networks. NeurIPS Chih-Kuan Yeh, Joon Sik Kim, Ian En-Hsu Yen, Pradeep Ravikumar
2018	J	`jnl`	Representer Point Selection for Explaining Deep Neural Networks. CoRR Chih-Kuan Yeh, Joon Sik Kim, Ian En-Hsu Yen, Pradeep Ravikumar
2018	J	`jnl`	Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability. CoRR Lingfei Wu, Ian En-Hsu Yen, Jie Chen, Rui Yan
2018	A*	`conf`	Scalable Spectral Clustering Using Random Binning Features. KDD Lingfei Wu, Pin-Yu Chen, Ian En-Hsu Yen, Fangli Xu, Yinglong Xia, Charu C. Aggarwal
2018	J	`jnl`	Scalable Spectral Clustering Using Random Binning Features. CoRR Lingfei Wu, Pin-Yu Chen, Ian En-Hsu Yen, Fangli Xu, Yinglong Xia, Charu C. Aggarwal
2018	—	`—`	Sublinear-Time Learning and Inference for High-Dimensional Models. Ian En-Hsu Yen
2018	A*	`conf`	Word Mover's Embedding: From Word2Vec to Document Embedding. EMNLP Lingfei Wu, Ian En-Hsu Yen, Kun Xu, Fangli Xu, Avinash Balakrishnan, Pin-Yu Chen, Pradeep Ravikumar, Michael J. Witbrock
2018	J	`jnl`	Word Mover's Embedding: From Word2Vec to Document Embedding. CoRR Lingfei Wu, Ian En-Hsu Yen, Kun Xu, Fangli Xu, Avinash Balakrishnan, Pin-Yu Chen, Pradeep Ravikumar, Michael J. Witbrock
2017	A*	`conf`	Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization. ICML Qi Lei, Ian En-Hsu Yen, Chao-Yuan Wu, Inderjit S. Dhillon, Pradeep Ravikumar
2017	A	`conf`	Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain. AISTATS Xiangru Huang, Ian En-Hsu Yen, Ruohan Zhang, Qixing Huang, Pradeep Ravikumar, Inderjit S. Dhillon
2017	A*	`conf`	Latent Feature Lasso. ICML Ian En-Hsu Yen, Wei-Cheng Lee, Sung-En Chang, Arun Sai Suggala, Shou-De Lin, Pradeep Ravikumar
2017	A*	`conf`	PPDsparse: A Parallel Primal-Dual Sparse Method for Extreme Classification. KDD Ian En-Hsu Yen, Xiangru Huang, Wei Dai, Pradeep Ravikumar, Inderjit S. Dhillon, Eric P. Xing
2017	A	`conf`	Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition. AISTATS Jiong Zhang, Ian En-Hsu Yen, Pradeep Ravikumar, Inderjit S. Dhillon
2016	A*	`conf`	A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery. ICML Ian En-Hsu Yen, Xin Lin, Jiong Zhang, Pradeep Ravikumar, Inderjit S. Dhillon
2016	—	`conf`	Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain. NIPS Ian En-Hsu Yen, Xiangru Huang, Kai Zhong, Ruohan Zhang, Pradeep Ravikumar, Inderjit S. Dhillon
2016	A	`conf`	Large-scale Submodular Greedy Exemplar Selection with Structured Similarity Matrices. UAI Dmitry Malioutov, Abhishek Kumar, Ian En-Hsu Yen
2016	A*	`conf`	PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification. ICML Ian En-Hsu Yen, Xiangru Huang, Pradeep Ravikumar, Kai Zhong, Inderjit S. Dhillon
2016	A*	`conf`	Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability. KDD Lingfei Wu, Ian En-Hsu Yen, Jie Chen, Rui Yan
2016	A	`conf`	Scalable Exemplar Clustering and Facility Location via Augmented Block Coordinate Descent with Column Generation. AISTATS Ian En-Hsu Yen, Dmitry Malioutov, Abhishek Kumar
2015	A*	`conf`	A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models. ICML Ian En-Hsu Yen, Xin Lin, Kai Zhong, Pradeep Ravikumar, Inderjit S. Dhillon
2015	—	`conf`	A Dual Augmented Block Minimization Framework for Learning with Limited Memory. NIPS Ian En-Hsu Yen, Shan-Wei Lin, Shou-De Lin
2015	—	`conf`	Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent. NIPS Ian En-Hsu Yen, Kai Zhong, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon
2015	A*	`conf`	Tackling the Achilles Heel of Social Networks: Influence Propagation based Language Model Smoothing. WWW Rui Yan, Ian En-Hsu Yen, Cheng-Te Li, Shiqi Zhao, Xiaohua Hu
2014	—	`conf`	Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings. NIPS Ian En-Hsu Yen, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon
2014	—	`conf`	Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators. NIPS Kai Zhong, Ian En-Hsu Yen, Inderjit S. Dhillon, Pradeep Ravikumar
2014	—	`conf`	Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space. NIPS Ian En-Hsu Yen, Ting-Wei Lin, Shou-De Lin, Pradeep Ravikumar, Inderjit S. Dhillon
2013	A*	`conf`	Indexed block coordinate descent for large-scale linear classification with limited memory. KDD Ian En-Hsu Yen, Chun-Fu Chang, Ting-Wei Lin, Shan-Wei Lin, Shou-De Lin